Internet Appendix: High Frequency Trading and Extreme Price Movements
|
|
- Deirdre Moody
- 5 years ago
- Views:
Transcription
1 Internet Appendix: High Frequency Trading and Extreme Price Movements This appendix includes two parts. First, it reports the results from the sample of EPMs defined as the 99.9 th percentile of raw returns. Second, it details the estimation of the Lee and Mykland (2012) jump identification algorithm and reports the results for EPMs defined using this algorithm. 1. EPM sample based on the 99.9 th percentile of raw returns As an alternative to the procedure that defines EPMs as the 99.9 th percentile of residuals from Eq. (1), we define EPMs as the 99.9 th percentile of raw returns. The results from this sample are reported in Tables A1-A7. The results obtained from both samples are qualitatively similar to those discussed in the main manuscript. 2. EPM sample based on the Lee-Mykland jump identification algorithm The Lee and Mykland (2012) algorithm (LM) identifies intervals with discrete price changes using the non-parametric approach based on realized volatility. First, the optimal sampling frequency is identified as for the 1 -dependent noise. In our sample, the second-by-second midpoint return observations tend to follow an MA process with many statistically significant dependent AR lags. Nonetheless, the magnitude of the lag coefficients tends to drop sharply after 6 8 lags. To make the estimated results comparable across stocks, we select 10. The algorithm suggests that a researcher should pre-average over M sampled observations before computing the jump statistics as follows: /, where is a parameter defined by the noise variance, and is the number of observations in the jump 1
2 estimation period. Recognizing that in a high-frequency sample the realized variation of logprices converges to noise variance, we estimate the volatility of noise as: /2 (A1) Following LM, we split each trading day into seven intervals: 9:30 10:00, 10:00 11:00, 11:00 12:00, 12:00 13:00, 13:00 14:00, 14:00 15:00, and 15:00 16:00. The estimated noise variance for these intervals is reported in Table A8. Based on the estimates, we choose 1/19, and therefore the estimated is close to one for all estimation periods. 1 Next, the standardized statistic for jump detection is defined as follows:, (A2) where, and is the estimate of the total variance. The total variance is a sum of the estimated noise variance and price volatility. We use the noise- and outlierrobust bipower variation of Christensen, Oomen and Podolskij (2014) as the measure of price volatility. From LM s Theorem 1, the null hypothesis of no jump in a given interval is rejected if:, (A3) where 2 (A4) 1 Since we use midpoint prices, much of the noise coming from the bid-ask bounce is mitigated, and the remaining noise does not produce the estimate high enough to make pre-sampling useful. 2
3 and. (A5) The rejection threshold is selected from the standard Gumbel distribution, which implies that β log log 1 α. We use the significance level of α 5%, leading to β The above procedure results in rejecting the no-jump hypothesis for 0.54% of sample observations. We restrict the LM sample to include the same number of EPMs as the 99.9 sample based on raw returns, or 45,406 instances, using the highest-magnitude LM jumps for each stock. In Tables A9 through A15, we use the LM sample to replicate the results reported in the main manuscript. Overall, the results in the LM sample are similar to those discussed in the main manuscript. References: Christensen, K., Oomen, R. C. A., Podolskij, M., Fact or friction: Jumps at ultra highfrequency. Journal of Financial Economics 114, Lee, S. S., Mykland, P. A., Jumps in equilibrium prices and market microstructure noise. Journal of Econometrics 168,
4 Table A1 Summary statistics The table reports summary statistics for the sample of extreme price movements (EPMs). is the absolute value of the 10-second midpoint return. is the number of (HFT) trades during the interval. and are the total dollar and share volume traded during the interval. and are quoted and relative quoted NBBO spreads, respectively, in dollars and percentage points. All statistics are averaged over the 10-second sampling intervals. Mean Median Std. dev. Absolute return, % Total trades Total HFT trades Dollar volume 473, ,158 1,024,504 Share volume 15,595 5,431 31,734 Quoted spread, $ Relative spread, % N 45,406 4
5 Table A2 Liquidity supply and demand around EPMs The table reports directional trading volume around extreme price movements. Time interval t is the 10-second EPM interval. In addition, we report the results for the two time intervals preceding the EPM and two subsequent time intervals. HFT D (nhft D ) is the difference in liquidity-demanding HFT (nhft) volume in the direction of the EPM and liquidity-demanding volume against the direction of the EPM. HFT S (nhft S ) is the difference in liquidity-providing volume against the direction of the EPM and liquidity-providing volume in the direction of the EPM. HFT NET (nhft NET ) is the difference between HFT D and HFT S (nhft D and nhft S ). -Values are in parentheses. *** and ** indicate statistical significance at the 1% and 5% levels. t-20 t-10 t t+10 t+20 HFT NET ** *** *** -42.7** (0.94) (0.04) (0.04) HFT D *** *** *** -99.1*** (0.13) HFT S *** *** 156.5*** 56.4*** (0.14) nhft NET ** 299.3*** 122.5*** 42.7** (0.94) (0.04) (0.04) nhft D 75.3** 326.7*** *** 672.4*** 317.0*** (0.03) nhft S -76.8** *** *** *** *** (0.02) 5
6 Table A3 Transitory and permanent EPMs The table reports summary statistics for transitory and permanent EPMs. Transitory EPMs revert by more than 2/3 of the EPM return in the following 30 minutes. Permanent EPMs do not revert by more than 1/3 in the same interval. Because we exclude EPMs that revert by the amount between 1/3 and 2/3, the total number of EPMs in this table is 87.60% of that reported in Panel A of Table A2. Panel B reports HFT NET around the two EPM types. Asterisks *** indicate statistical significance at the 1% level. Transitory Permanent Mean Std. dev. Mean Std. dev. Absolute return, % Total trades Total HFT trades Dollar volume 465,100 1,058, , ,642 Share volume 14,716 29,412 14,652 29,098 Quoted spread, $ Relative spr., % N 18,185 21,116 t-20 t-10 t t+10 t+20 Transitory *** *** *** Permanent *** *** *** 2.0 6
7 Table A4 EPM magnitude quartiles Panel A divides EPMs into quartiles by return magnitude, from smallest to largest. Panel B contains HFT NET statistics. Asterisks ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels. Q1 (small) Q2 Mean Std. dev. Mean Std. dev. Absolute return, % Total trades Total HFT trades Dollar volume 378, , , ,376 Share volume 12,487 24,759 12,973 24,216 Quoted spread, $ Relative spr., % N 11,358 11,327 Q3 Q4 (large) Absolute return, % Total trades Total HFT trades Dollar volume 452, , ,912 1,356,125 Share volume 15,070 30,330 21,842 43,031 Quoted spread, $ Relative spr., % N 11,358 11,363 t-20 t-10 t t+10 t+20 Q * *** 4.9 Q *** *** -82.2** -61.8* Q *** -82.8* Q *** ***
8 Table A5 Standalone and co-epms Panel A divides EPMs into standalone and co-epms, with the latter group capturing EPMs that occur simultaneously in several stocks. Panel B contains HFT NET statistics. Asterisks *** and ** indicate statistical significance at the 1% and 5% levels. Standalone Co-EPMs Mean Std. dev. Mean Std. dev. Absolute return, % Total trades Total HFT trades Dollar volume 625,553 1,272, , ,887 Share volume 21,368 40,535 11,280 22,092 Quoted spread, $ Relative spr., % # Stocks N 19,424 25,982 t-20 t-10 t t+10 t+20 Standalone *** *** Co-EPMs *** 446.4*** *** -44.1** 8
9 Table A6 Net HFT activity and EPMs The table reports estimated coefficients from the following regression: 1, where HFT NET is the difference between HFT D and HFT S ; the dummy 1 EPM is equal to one if a 10- second interval t is identified to contain an EPM and is equal to zero otherwise; 1 EPM-TRANSITORY and 1 EPM-PERMANENT are dummies that capture the two EPM types; 1 EPM-STANDALONE captures the standalone EPMs; 1 CO-EPM captures EPMs that occur simultaneously in two or more sample stocks; 1 EPM-Q1 through 1 EPM-Q4 identify four EPM quartiles by magnitude, from the smallest to the largest; Ret is the absolute return; Vol is the total trading volume; Spr is the percentage quoted spread; and is a vector of lags of the dependent variable and each of the independent variables, with 1,2,,10 and the variables indexed with a subscript. All non-dummy variables are standardized on the stock level. Regressions are estimated with stock fixed effects. -Values associated with the double-clustered standard errors are in parentheses. *** denote statistical significance at the 1% level. (1) (2) (3) (4) 1 EPM *** 1 EPM-TRANSITORY *** 1 EPM-PERMANENT *** 1 EPM-STANDALONE *** 1 CO-EPM *** 1 EPM-Q *** 1 EPM-Q *** 1 EPM-Q *** 1 EPM-Q *** Ret 0.072*** 0.072*** 0.072*** 0.073*** Vol 0.081*** 0.081*** 0.081*** 0.081*** Spr *** *** *** *** Adj. R
10 Table A7 EPM determinants The table reports the coefficients and the marginal effects from a probit model of EPM occurrence: 1, where the dependent variable is equal to one if an interval contains an extreme price movement and zero otherwise. All independent variables are lagged by one interval. HFT NET is the share volume traded in the direction of the price movement minus the share volume traded against the direction of the price movement for all HFT trades, Ret is the absolute return, Vol is total traded volume, Spr is the percentage quoted spread. All variables are standardized on the stock level. The marginal effects are scaled by a factor of 1,000. -Values are in parentheses. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels. All Standalone Co-EPMs Permanent Transitory (1) (2) (3) (4) (5) Intercept *** *** *** *** *** HFT NET t *** *** * *** Marginal Effect (0.42) (0.09) Controls Yes Yes Yes Yes Yes Pseudo-R
11 Table A8 Noise volatility The table reports estimated noise volatility q as a percentage of price used for computation of the LM statistic. The noise variance is estimated by intraday period, day and stock. Period q Max Std. Dev. 9:30 10: % 0.095% 0.014% 10:00 11: % 0.068% 0.010% 11:00 12: % 0.051% 0.008% 12:00 13: % 0.043% 0.006% 13:00 14: % 0.042% 0.006% 14:00 15: % 0.047% 0.007% 15:00 16: % 0.054% 0.008% 11
12 Table A9 Summary statistics The table reports summary statistics for the sample of extreme price movements (EPMs). is the absolute value of the 10-second midpoint return. is the number of (HFT) trades during the interval. and are the total dollar and share volume traded during the interval. and are quoted and relative quoted NBBO spreads, respectively, in dollars and percentage points. All statistics are averaged over the 10-second sampling intervals. Mean Median Std. dev. Absolute return, % Total trades Total HFT trades Dollar volume 531, ,249 1,007,435 Share volume 16,688 6,189 31,989 Quoted spread, $ Relative spread, % N 45,400 12
13 Table A10 Liquidity supply and demand around EPMs The table reports directional trading volume around extreme price movements. Time interval t is the 10-second EPM interval. In addition, we report the results for the two time intervals preceding the EPM and two subsequent time intervals. HFT D (nhft D ) is the difference in liquidity-demanding HFT (nhft) volume in the direction of the EPM and liquidity-demanding volume against the direction of the EPM. HFT S (nhft S ) is the difference in liquidity-providing volume against the direction of the EPM and liquidity-providing volume in the direction of the EPM. HFT NET (nhft NET ) is the difference between HFT D and HFT S (nhft D and nhft S ). -Values are in parentheses. *** and ** indicate statistical significance at the 1% and 5% levels. t-20 t-10 t t+10 t+20 HFT NET 30.5** 40.6** *** -65.1*** 7.1 (0.04) (0.02) (0.66) HFT D *** *** *** *** (0.53) HFT S *** *** 297.9*** 153.2*** (0.14) nhft NET -30.5** -40.6** 892.6*** 65.1*** -7.1 (0.04) (0.02) (0.66) nhft D *** *** 478.9*** 145.8*** (0.84) nhft S *** *** *** *** (0.14) 13
14 Table A11 Transitory and permanent EPMs The table reports summary statistics for transitory and permanent EPMs. Transitory EPMs revert by more than 2/3 of the EPM return in the following 30 minutes. Permanent EPMs do not revert by more than 1/3 in the same interval. Because we exclude EPMs that revert by the amount between 1/3 and 2/3, the total number of EPMs in this table is 87.60% of that reported in Panel A of Table A2. Panel B reports HFT NET around the two EPM types. Asterisks *** and ** indicate statistical significance at the 1% and 5% levels. Transitory Termanent Mean Std. dev. Mean Std. dev. Absolute return, % Total trades Total HFT trades Dollar volume 513,793 1,014, , ,796 Share volume 15,400 28,963 14,929 27,001 Quoted spread, $ Relative spr., % N 18,249 21,523 Panel B: HFT NET t-20 t-10 t t+10 t+20 Transitory *** -75.1*** -3.4 Permanent *** -61.6**
15 Table A12 EPM magnitude quartiles Panel A divides EPMs into quartiles by return magnitude, from smallest to largest. Panel B contains HFT NET statistics. Asterisks ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels. Q1 (small) Q2 Mean Std. dev. Mean Std. Dev. Absolute return, % Total trades Total HFT trades Dollar volume 429, , , ,772 Share volume 12,861 22,658 14,437 27,024 Quoted spread, $ Relative spr., % N 11,313 11,366 Q3 Q4 (large) Absolute return, % Total trades Total HFT trades Dollar volume 512, , ,441 1,403,664 Share volume 16,194 28,929 23,240 44,130 Quoted spread, $ Relative spr., % N 11,355 11,366 t-20 t-10 t t+10 t+20 Q *** Q2 81.7*** *** Q * *** -77.3** 25.7 Q *** ***
16 Table A13 Standalone and co-epms Panel A divides EPMs into standalone and co-epms, with the latter group capturing EPMs that occur simultaneously in several stocks. Panel B contains HFT NET statistics. Asterisks *** and ** indicate statistical significance at the 1% and 5% levels. Standalone Co-EPMs Mean Std. dev. Mean Std. dev. Absolute return, % Total trades Total HFT trades Dollar volume 590,320 1,091, , ,154 Share volume 18,342 33,310 13,552 29,064 Quoted spread, $ Relative spr., % # Stocks N 29,724 15,676 t-20 t-10 t t+10 t+20 Standalone *** -62.2** 27.9 Co-EPMs *** 850.7*** -70.6***
17 Table A14 Net HFT activity and EPMs The table reports estimated coefficients from the following regression: 1, where HFT NET is the difference between HFT D and HFT S ; the dummy 1 EPM is equal to one if a 10- second interval t is identified to contain an EPM and is equal to zero otherwise; 1 EPM-TRANSITORY and 1 EPM-PERMANENT are dummies that capture the two EPM types; 1 EPM-STANDALONE captures the standalone EPMs; 1 CO-EPM captures EPMs that occur simultaneously in two or more sample stocks; 1 EPM-Q1 through 1 EPM-Q4 identify four EPM quartiles by magnitude, from the smallest to the largest; Ret is the absolute return; Vol is the total trading volume; Spr is the percentage quoted spread; and is a vector of lags of the dependent variable and each of the independent variables, with 1,2,,10 and the variables indexed with a subscript. All non-dummy variables are standardized on the stock level. Regressions are estimated with stock fixed effects. -Values associated with the double-clustered standard errors are in parentheses. *** denote statistical significance at the 1% level. (1) (2) (3) (4) 1 EPM *** 1 EPM-TRANSITORY *** 1 EPM-PERMANENT *** 1 EPM-STANDALONE *** 1 CO-EPM (0.07) 1 EPM-Q *** 1 EPM-Q *** 1 EPM-Q *** 1 EPM-Q *** Ret 0.072*** 0.072*** 0.071*** 0.073*** Vol 0.083*** 0.083*** 0.083*** 0.083*** Spr *** *** *** *** Adj. R
18 Table A15 EPM determinants The table reports the coefficients and the marginal effects from a probit model of EPM occurrence: 1, where the dependent variable is equal to one if an interval contains an extreme price movement and zero otherwise. All independent variables are lagged by one interval. HFT NET is the share volume traded in the direction of the price movement minus the share volume traded against the direction of the price movement for all HFT trades, Ret is the absolute return, Vol is total traded volume, Spr is the percentage quoted spread. All variables are standardized on the stock level. The marginal effects are scaled by a factor of 1, Values are in parentheses. *** and * indicate statistical significance at the 1% and 10% levels. All Standalone Co-EPMs Permanent Transitory (1) (2) (3) (4) (5) Intercept *** *** *** *** *** HFT NET t *** * Marginal effect (0.58) (0.21) (0.06) (0.18) Controls Yes Yes Yes Yes Yes Pseudo-R
1. Logit and Linear Probability Models
INTERNET APPENDIX 1. Logit and Linear Probability Models Table 1 Leverage and the Likelihood of a Union Strike (Logit Models) This table presents estimation results of logit models of union strikes during
More informationChapter 4 Level of Volatility in the Indian Stock Market
Chapter 4 Level of Volatility in the Indian Stock Market Measurement of volatility is an important issue in financial econometrics. The main reason for the prominent role that volatility plays in financial
More informationOnline Appendix to. The Value of Crowdsourced Earnings Forecasts
Online Appendix to The Value of Crowdsourced Earnings Forecasts This online appendix tabulates and discusses the results of robustness checks and supplementary analyses mentioned in the paper. A1. Estimating
More informationDeviations from Optimal Corporate Cash Holdings and the Valuation from a Shareholder s Perspective
Deviations from Optimal Corporate Cash Holdings and the Valuation from a Shareholder s Perspective Zhenxu Tong * University of Exeter Abstract The tradeoff theory of corporate cash holdings predicts that
More informationMarket Integration and High Frequency Intermediation*
Market Integration and High Frequency Intermediation* Jonathan Brogaard Terrence Hendershott Ryan Riordan First Draft: November 2014 Current Draft: November 2014 Abstract: To date, high frequency trading
More informationEmpirical Asset Pricing for Tactical Asset Allocation
Introduction Process Model Conclusion Department of Finance The University of Connecticut School of Business stephen.r.rush@gmail.com May 10, 2012 Background Portfolio Managers Want to justify fees with
More informationIndian Institute of Management Calcutta. Working Paper Series. WPS No. 797 March Implied Volatility and Predictability of GARCH Models
Indian Institute of Management Calcutta Working Paper Series WPS No. 797 March 2017 Implied Volatility and Predictability of GARCH Models Vivek Rajvanshi Assistant Professor, Indian Institute of Management
More informationFolia Oeconomica Stetinensia DOI: /foli EURUSD INTRADAY PRICE REVERSAL
Folia Oeconomica Stetinensia DOI: 10.1515/foli-2015-0014 EURUSD INTRADAY PRICE REVERSAL Marta Wiśniewska, Ph.D. Gdansk School of Banking Dolna Brama 8, 80-821 Gdańsk, Poland e-mail: marta@witor.biz Received
More informationTable IA.1 CEO Pay-Size Elasticity and Increased Labor Demand Panel A: IPOs Scaled by Full Sample Industry Average
Table IA.1 CEO Pay-Size Elasticity and Increased Labor Demand Panel A: IPOs Scaled by Industry Average (1) (2) (3) (4) (5) Ln(Market Value) 0.423 0.419 0.423 0.423 0.255 (33.29) (30.84) (33.29) (33.29)
More informationUniversité de Montréal. Rapport de recherche. Empirical Analysis of Jumps Contribution to Volatility Forecasting Using High Frequency Data
Université de Montréal Rapport de recherche Empirical Analysis of Jumps Contribution to Volatility Forecasting Using High Frequency Data Rédigé par : Imhof, Adolfo Dirigé par : Kalnina, Ilze Département
More informationThe Great Moderation Flattens Fat Tails: Disappearing Leptokurtosis
The Great Moderation Flattens Fat Tails: Disappearing Leptokurtosis WenShwo Fang Department of Economics Feng Chia University 100 WenHwa Road, Taichung, TAIWAN Stephen M. Miller* College of Business University
More informationa. Explain why the coefficients change in the observed direction when switching from OLS to Tobit estimation.
1. Using data from IRS Form 5500 filings by U.S. pension plans, I estimated a model of contributions to pension plans as ln(1 + c i ) = α 0 + U i α 1 + PD i α 2 + e i Where the subscript i indicates the
More informationEconomics 201FS: Variance Measures and Jump Testing
1/32 : Variance Measures and Jump Testing George Tauchen Duke University January 21 1. Introduction and Motivation 2/32 Stochastic volatility models account for most of the anomalies in financial price
More informationAssessing Model Stability Using Recursive Estimation and Recursive Residuals
Assessing Model Stability Using Recursive Estimation and Recursive Residuals Our forecasting procedure cannot be expected to produce good forecasts if the forecasting model that we constructed was stable
More informationThe University of Chicago, Booth School of Business Business 41202, Spring Quarter 2009, Mr. Ruey S. Tsay. Solutions to Final Exam
The University of Chicago, Booth School of Business Business 41202, Spring Quarter 2009, Mr. Ruey S. Tsay Solutions to Final Exam Problem A: (42 pts) Answer briefly the following questions. 1. Questions
More informationInternet Appendix to Is Information Risk Priced? Evidence from Abnormal Idiosyncratic Volatility
Internet Appendix to Is Information Risk Priced? Evidence from Abnormal Idiosyncratic Volatility Table IA.1 Further Summary Statistics This table presents the summary statistics of further variables used
More informationMarket Microstructure Invariants
Market Microstructure Invariants Albert S. Kyle and Anna A. Obizhaeva University of Maryland TI-SoFiE Conference 212 Amsterdam, Netherlands March 27, 212 Kyle and Obizhaeva Market Microstructure Invariants
More informationOptimal Debt-to-Equity Ratios and Stock Returns
Utah State University DigitalCommons@USU All Graduate Plan B and other Reports Graduate Studies 5-2014 Optimal Debt-to-Equity Ratios and Stock Returns Courtney D. Winn Utah State University Follow this
More informationLecture 9: Markov and Regime
Lecture 9: Markov and Regime Switching Models Prof. Massimo Guidolin 20192 Financial Econometrics Spring 2017 Overview Motivation Deterministic vs. Endogeneous, Stochastic Switching Dummy Regressiom Switching
More informationDepression Babies: Do Macroeconomic Experiences Affect Risk-Taking?
Depression Babies: Do Macroeconomic Experiences Affect Risk-Taking? October 19, 2009 Ulrike Malmendier, UC Berkeley (joint work with Stefan Nagel, Stanford) 1 The Tale of Depression Babies I don t know
More informationA Blessing or a Curse? The Impact of High Frequency Trading on Institutional Investors
Second Annual Conference on Financial Market Regulation, May 1, 2015 A Blessing or a Curse? The Impact of High Frequency Trading on Institutional Investors Lin Tong Fordham University Characteristics and
More informationALL THINGS CONSIDERED, TAXES DRIVE THE JANUARY EFFECT. Abstract
The Journal of Financial Research Vol. XXVII, No. 3 Pages 351 372 Fall 2004 ALL THINGS CONSIDERED, TAXES DRIVE THE JANUARY EFFECT Honghui Chen University of Central Florida Vijay Singal Virginia Tech Abstract
More informationFinancial Econometrics Jeffrey R. Russell. Midterm 2014 Suggested Solutions. TA: B. B. Deng
Financial Econometrics Jeffrey R. Russell Midterm 2014 Suggested Solutions TA: B. B. Deng Unless otherwise stated, e t is iid N(0,s 2 ) 1. (12 points) Consider the three series y1, y2, y3, and y4. Match
More informationMarket Efficiency and Microstructure Evolution in U.S. Equity Markets: A High-Frequency Perspective
Market Efficiency and Microstructure Evolution in U.S. Equity Markets: A High-Frequency Perspective Jeff Castura, Robert Litzenberger, Richard Gorelick, Yogesh Dwivedi RGM Advisors, LLC August 30, 2010
More informationEconometric Methods for Valuation Analysis
Econometric Methods for Valuation Analysis Margarita Genius Dept of Economics M. Genius (Univ. of Crete) Econometric Methods for Valuation Analysis Cagliari, 2017 1 / 25 Outline We will consider econometric
More informationLecture 8: Markov and Regime
Lecture 8: Markov and Regime Switching Models Prof. Massimo Guidolin 20192 Financial Econometrics Spring 2016 Overview Motivation Deterministic vs. Endogeneous, Stochastic Switching Dummy Regressiom Switching
More informationJumps in Equilibrium Prices. and Market Microstructure Noise
Jumps in Equilibrium Prices and Market Microstructure Noise Suzanne S. Lee and Per A. Mykland Abstract Asset prices we observe in the financial markets combine two unobservable components: equilibrium
More informationThe Random Walk Hypothesis in Emerging Stock Market-Evidence from Nonlinear Fourier Unit Root Test
, July 6-8, 2011, London, U.K. The Random Walk Hypothesis in Emerging Stock Market-Evidence from Nonlinear Fourier Unit Root Test Seyyed Ali Paytakhti Oskooe Abstract- This study adopts a new unit root
More informationEstimating the Dynamics of Volatility. David A. Hsieh. Fuqua School of Business Duke University Durham, NC (919)
Estimating the Dynamics of Volatility by David A. Hsieh Fuqua School of Business Duke University Durham, NC 27706 (919)-660-7779 October 1993 Prepared for the Conference on Financial Innovations: 20 Years
More informationA Note on the Oil Price Trend and GARCH Shocks
A Note on the Oil Price Trend and GARCH Shocks Jing Li* and Henry Thompson** This paper investigates the trend in the monthly real price of oil between 1990 and 2008 with a generalized autoregressive conditional
More informationPublic-private sector pay differential in UK: A recent update
Public-private sector pay differential in UK: A recent update by D H Blackaby P D Murphy N C O Leary A V Staneva No. 2013-01 Department of Economics Discussion Paper Series Public-private sector pay differential
More informationBooth School of Business, University of Chicago Business 41202, Spring Quarter 2016, Mr. Ruey S. Tsay. Solutions to Midterm
Booth School of Business, University of Chicago Business 41202, Spring Quarter 2016, Mr. Ruey S. Tsay Solutions to Midterm Problem A: (30 pts) Answer briefly the following questions. Each question has
More informationEconometric Methods for Valuation Analysis
Econometric Methods for Valuation Analysis Margarita Genius Dept of Economics M. Genius (Univ. of Crete) Econometric Methods for Valuation Analysis Cagliari, 2017 1 / 26 Correlation Analysis Simple Regression
More informationExamining the impact of macroeconomic announcements on gold futures in a VAR-GARCH framework
Article Title: Author Details: Examining the impact of macroeconomic announcements on gold futures in a VAR-GARCH framework **Dr. Lee A. Smales, School of Economics & Finance, Curtin University, Perth,
More informationReview questions for Multinomial Logit/Probit, Tobit, Heckit, Quantile Regressions
1. I estimated a multinomial logit model of employment behavior using data from the 2006 Current Population Survey. The three possible outcomes for a person are employed (outcome=1), unemployed (outcome=2)
More informationETF Volatility around the New York Stock Exchange Close.
San Jose State University From the SelectedWorks of Stoyu I. Ivanov 2011 ETF Volatility around the New York Stock Exchange Close. Stoyu I. Ivanov, San Jose State University Available at: https://works.bepress.com/stoyu-ivanov/15/
More informationA Closer Look at High-Frequency Data and Volatility Forecasting in a HAR Framework 1
A Closer Look at High-Frequency Data and Volatility Forecasting in a HAR Framework 1 Derek Song ECON 21FS Spring 29 1 This report was written in compliance with the Duke Community Standard 2 1. Introduction
More informationThe University of Chicago, Booth School of Business Business 41202, Spring Quarter 2017, Mr. Ruey S. Tsay. Solutions to Final Exam
The University of Chicago, Booth School of Business Business 41202, Spring Quarter 2017, Mr. Ruey S. Tsay Solutions to Final Exam Problem A: (40 points) Answer briefly the following questions. 1. Describe
More informationThe data definition file provided by the authors is reproduced below: Obs: 1500 home sales in Stockton, CA from Oct 1, 1996 to Nov 30, 1998
Economics 312 Sample Project Report Jeffrey Parker Introduction This project is based on Exercise 2.12 on page 81 of the Hill, Griffiths, and Lim text. It examines how the sale price of houses in Stockton,
More informationResearch Article The Volatility of the Index of Shanghai Stock Market Research Based on ARCH and Its Extended Forms
Discrete Dynamics in Nature and Society Volume 2009, Article ID 743685, 9 pages doi:10.1155/2009/743685 Research Article The Volatility of the Index of Shanghai Stock Market Research Based on ARCH and
More informationInternet Appendix for: Change You Can Believe In? Hedge Fund Data Revisions
Internet Appendix for: Change You Can Believe In? Hedge Fund Data Revisions Andrew J. Patton, Tarun Ramadorai, Michael P. Streatfield 22 March 2013 Appendix A The Consolidated Hedge Fund Database... 2
More informationInverse ETFs and Market Quality
Utah State University DigitalCommons@USU All Graduate Plan B and other Reports Graduate Studies 5-215 Inverse ETFs and Market Quality Darren J. Woodward Utah State University Follow this and additional
More informationDaily Data is Bad for Beta: Opacity and Frequency-Dependent Betas Online Appendix
Daily Data is Bad for Beta: Opacity and Frequency-Dependent Betas Online Appendix Thomas Gilbert Christopher Hrdlicka Jonathan Kalodimos Stephan Siegel December 17, 2013 Abstract In this Online Appendix,
More informationSources of Financing in Different Forms of Corporate Liquidity and the Performance of M&As
Sources of Financing in Different Forms of Corporate Liquidity and the Performance of M&As Zhenxu Tong * University of Exeter Jian Liu ** University of Exeter This draft: August 2016 Abstract We examine
More informationInternet Appendix to. Glued to the TV: Distracted Noise Traders and Stock Market Liquidity
Internet Appendix to Glued to the TV: Distracted Noise Traders and Stock Market Liquidity Joel PERESS & Daniel SCHMIDT 6 October 2018 1 Table of Contents Internet Appendix A: The Implications of Distraction
More informationPremium Timing with Valuation Ratios
RESEARCH Premium Timing with Valuation Ratios March 2016 Wei Dai, PhD Research The predictability of expected stock returns is an old topic and an important one. While investors may increase expected returns
More informationEstimation of High-Frequency Volatility: An Autoregressive Conditional Duration Approach
Estimation of High-Frequency Volatility: An Autoregressive Conditional Duration Approach Yiu-Kuen Tse School of Economics, Singapore Management University Thomas Tao Yang Department of Economics, Boston
More informationBessembinder / Zhang (2013): Firm characteristics and long-run stock returns after corporate events. Discussion by Henrik Moser April 24, 2015
Bessembinder / Zhang (2013): Firm characteristics and long-run stock returns after corporate events Discussion by Henrik Moser April 24, 2015 Motivation of the paper 3 Authors review the connection of
More informationBooth School of Business, University of Chicago Business 41202, Spring Quarter 2014, Mr. Ruey S. Tsay. Solutions to Midterm
Booth School of Business, University of Chicago Business 41202, Spring Quarter 2014, Mr. Ruey S. Tsay Solutions to Midterm Problem A: (30 pts) Answer briefly the following questions. Each question has
More informationEquity Price Dynamics Before and After the Introduction of the Euro: A Note*
Equity Price Dynamics Before and After the Introduction of the Euro: A Note* Yin-Wong Cheung University of California, U.S.A. Frank Westermann University of Munich, Germany Daily data from the German and
More informationImplied Volatility v/s Realized Volatility: A Forecasting Dimension
4 Implied Volatility v/s Realized Volatility: A Forecasting Dimension 4.1 Introduction Modelling and predicting financial market volatility has played an important role for market participants as it enables
More informationBooth School of Business, University of Chicago Business 41202, Spring Quarter 2010, Mr. Ruey S. Tsay. Solutions to Midterm
Booth School of Business, University of Chicago Business 41202, Spring Quarter 2010, Mr. Ruey S. Tsay Solutions to Midterm Problem A: (30 pts) Answer briefly the following questions. Each question has
More informationInferring Trader Behavior from Transaction Data: A Simple Model
Inferring Trader Behavior from Transaction Data: A Simple Model by David Jackson* First draft: May 08, 2003 This draft: May 08, 2003 * Sprott School of Business Telephone: (613) 520-2600 Ext. 2383 Carleton
More informationA Note on the Oil Price Trend and GARCH Shocks
MPRA Munich Personal RePEc Archive A Note on the Oil Price Trend and GARCH Shocks Li Jing and Henry Thompson 2010 Online at http://mpra.ub.uni-muenchen.de/20654/ MPRA Paper No. 20654, posted 13. February
More informationDan Breznitz Munk School of Global Affairs, University of Toronto, 1 Devonshire Place, Toronto, Ontario M5S 3K7 CANADA
RESEARCH ARTICLE THE ROLE OF VENTURE CAPITAL IN THE FORMATION OF A NEW TECHNOLOGICAL ECOSYSTEM: EVIDENCE FROM THE CLOUD Dan Breznitz Munk School of Global Affairs, University of Toronto, 1 Devonshire Place,
More informationBESSH-16. FULL PAPER PROCEEDING Multidisciplinary Studies Available online at
FULL PAPER PROEEDING Multidisciplinary Studies Available online at www.academicfora.com Full Paper Proceeding BESSH-2016, Vol. 76- Issue.3, 15-23 ISBN 978-969-670-180-4 BESSH-16 A STUDY ON THE OMPARATIVE
More informationHedge Funds as International Liquidity Providers: Evidence from Convertible Bond Arbitrage in Canada
Hedge Funds as International Liquidity Providers: Evidence from Convertible Bond Arbitrage in Canada Evan Gatev Simon Fraser University Mingxin Li Simon Fraser University AUGUST 2012 Abstract We examine
More informationThe Persistent Effect of Temporary Affirmative Action: Online Appendix
The Persistent Effect of Temporary Affirmative Action: Online Appendix Conrad Miller Contents A Extensions and Robustness Checks 2 A. Heterogeneity by Employer Size.............................. 2 A.2
More informationInvestment Platforms Market Study Interim Report: Annex 7 Fund Discounts and Promotions
MS17/1.2: Annex 7 Market Study Investment Platforms Market Study Interim Report: Annex 7 Fund Discounts and Promotions July 2018 Annex 7: Introduction 1. There are several ways in which investment platforms
More informationThe Vasicek adjustment to beta estimates in the Capital Asset Pricing Model
The Vasicek adjustment to beta estimates in the Capital Asset Pricing Model 17 June 2013 Contents 1. Preparation of this report... 1 2. Executive summary... 2 3. Issue and evaluation approach... 4 3.1.
More informationReturn Volatility, Market Microstructure Noise, and Institutional Investors: Evidence from High Frequency Market
Return Volatility, Market Microstructure Noise, and Institutional Investors: Evidence from High Frequency Market Yuting Tan, Lan Zhang R/Finance 2017 ytan36@uic.edu May 19, 2017 Yuting Tan, Lan Zhang (UIC)
More informationFinancial liberalization and the relationship-specificity of exports *
Financial and the relationship-specificity of exports * Fabrice Defever Jens Suedekum a) University of Nottingham Center of Economic Performance (LSE) GEP and CESifo Mercator School of Management University
More informationFinal Exam Suggested Solutions
University of Washington Fall 003 Department of Economics Eric Zivot Economics 483 Final Exam Suggested Solutions This is a closed book and closed note exam. However, you are allowed one page of handwritten
More informationJournal of Economics and Financial Analysis, Vol:1, No:1 (2017) 1-13
Journal of Economics and Financial Analysis, Vol:1, No:1 (2017) 1-13 Journal of Economics and Financial Analysis Type: Double Blind Peer Reviewed Scientific Journal Printed ISSN: 2521-6627 Online ISSN:
More informationGraduate School of Business, University of Chicago Business 41202, Spring Quarter 2007, Mr. Ruey S. Tsay. Solutions to Final Exam
Graduate School of Business, University of Chicago Business 41202, Spring Quarter 2007, Mr. Ruey S. Tsay Solutions to Final Exam Problem A: (30 pts) Answer briefly the following questions. 1. Suppose that
More informationBooth School of Business, University of Chicago Business 41202, Spring Quarter 2012, Mr. Ruey S. Tsay. Solutions to Midterm
Booth School of Business, University of Chicago Business 41202, Spring Quarter 2012, Mr. Ruey S. Tsay Solutions to Midterm Problem A: (34 pts) Answer briefly the following questions. Each question has
More informationTick size and trading costs on the Korea Stock Exchange
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/228723439 Tick size and trading costs on the Korea Stock Exchange Article January 2005 CITATIONS
More informationFinal Exam - section 1. Thursday, December hours, 30 minutes
Econometrics, ECON312 San Francisco State University Michael Bar Fall 2013 Final Exam - section 1 Thursday, December 19 1 hours, 30 minutes Name: Instructions 1. This is closed book, closed notes exam.
More informationIntraday arbitrage opportunities of basis trading in current futures markets: an application of. the threshold autoregressive model.
Intraday arbitrage opportunities of basis trading in current futures markets: an application of the threshold autoregressive model Chien-Ho Wang Department of Economics, National Taipei University, 151,
More informationGDP, Share Prices, and Share Returns: Australian and New Zealand Evidence
Journal of Money, Investment and Banking ISSN 1450-288X Issue 5 (2008) EuroJournals Publishing, Inc. 2008 http://www.eurojournals.com/finance.htm GDP, Share Prices, and Share Returns: Australian and New
More informationEstimating Bivariate GARCH-Jump Model Based on High Frequency Data : the case of revaluation of Chinese Yuan in July 2005
Estimating Bivariate GARCH-Jump Model Based on High Frequency Data : the case of revaluation of Chinese Yuan in July 2005 Xinhong Lu, Koichi Maekawa, Ken-ichi Kawai July 2006 Abstract This paper attempts
More informationLecture 5a: ARCH Models
Lecture 5a: ARCH Models 1 2 Big Picture 1. We use ARMA model for the conditional mean 2. We use ARCH model for the conditional variance 3. ARMA and ARCH model can be used together to describe both conditional
More informationLarge price movements and short-lived changes in spreads, volume, and selling pressure
The Quarterly Review of Economics and Finance 39 (1999) 303 316 Large price movements and short-lived changes in spreads, volume, and selling pressure Raymond M. Brooks a, JinWoo Park b, Tie Su c, * a
More informationThe University of Chicago, Booth School of Business Business 41202, Spring Quarter 2010, Mr. Ruey S. Tsay Solutions to Final Exam
The University of Chicago, Booth School of Business Business 410, Spring Quarter 010, Mr. Ruey S. Tsay Solutions to Final Exam Problem A: (4 pts) Answer briefly the following questions. 1. Questions 1
More informationNumerical Descriptions of Data
Numerical Descriptions of Data Measures of Center Mean x = x i n Excel: = average ( ) Weighted mean x = (x i w i ) w i x = data values x i = i th data value w i = weight of the i th data value Median =
More informationMeasurement Effects and the Variance of Returns After Stock Splits and Stock Dividends
Measurement Effects and the Variance of Returns After Stock Splits and Stock Dividends Jennifer Lynch Koski University of Washington This article examines the relation between two factors affecting stock
More informationTable I Descriptive Statistics This table shows the breakdown of the eligible funds as at May 2011. AUM refers to assets under management. Panel A: Fund Breakdown Fund Count Vintage count Avg AUM US$ MM
More informationMarket Frictions, Price Delay, and the Cross-Section of Expected Returns
Market Frictions, Price Delay, and the Cross-Section of Expected Returns forthcoming The Review of Financial Studies Kewei Hou Fisher College of Business Ohio State University and Tobias J. Moskowitz Graduate
More informationScarcity effects of QE: A transaction-level analysis in the Bund market
Scarcity effects of QE: A transaction-level analysis in the Bund market Kathi Schlepper Heiko Hofer Ryan Riordan Andreas Schrimpf Deutsche Bundesbank Deutsche Bundesbank Queen s University Bank for International
More informationCHAPTER 6 DETERMINANTS OF LIQUIDITY COMMONALITY ON NATIONAL STOCK EXCHANGE OF INDIA
CHAPTER 6 DETERMINANTS OF LIQUIDITY COMMONALITY ON NATIONAL STOCK EXCHANGE OF INDIA 6.1 Introduction In the previous chapter, we established that liquidity commonality exists in the context of an order-driven
More informationIN THE REGULAR AND ALEXANDER KUROV*
TICK SIZE REDUCTION, EXECUTION COSTS, AND INFORMATIONAL EFFICIENCY IN THE REGULAR AND E-MINI NASDAQ-100 INDEX FUTURES MARKETS ALEXANDER KUROV* On April 2, 2006, the Chicago Mercantile Exchange reduced
More informationDescriptive Statistics
Petra Petrovics Descriptive Statistics 2 nd seminar DESCRIPTIVE STATISTICS Definition: Descriptive statistics is concerned only with collecting and describing data Methods: - statistical tables and graphs
More informationFinancial Econometrics Jeffrey R. Russell Midterm 2014
Name: Financial Econometrics Jeffrey R. Russell Midterm 2014 You have 2 hours to complete the exam. Use can use a calculator and one side of an 8.5x11 cheat sheet. Try to fit all your work in the space
More informationMarket Integration, Price Discovery, and Volatility in Agricultural Commodity Futures P.Ramasundaram* and Sendhil R**
Market Integration, Price Discovery, and Volatility in Agricultural Commodity Futures P.Ramasundaram* and Sendhil R** *National Coordinator (M&E), National Agricultural Innovation Project (NAIP), Krishi
More informationInternet Appendix to Credit Ratings and the Cost of Municipal Financing 1
Internet Appendix to Credit Ratings and the Cost of Municipal Financing 1 April 30, 2017 This Internet Appendix contains analyses omitted from the body of the paper to conserve space. Table A.1 displays
More informationChapter 3 Descriptive Statistics: Numerical Measures Part A
Slides Prepared by JOHN S. LOUCKS St. Edward s University Slide 1 Chapter 3 Descriptive Statistics: Numerical Measures Part A Measures of Location Measures of Variability Slide Measures of Location Mean
More informationInternet Appendix to The Booms and Busts of Beta Arbitrage
Internet Appendix to The Booms and Busts of Beta Arbitrage Table A1: Event Time CoBAR This table reports some basic statistics of CoBAR, the excess comovement among low beta stocks over the period 1970
More informationCash holdings determinants in the Portuguese economy 1
17 Cash holdings determinants in the Portuguese economy 1 Luísa Farinha Pedro Prego 2 Abstract The analysis of liquidity management decisions by firms has recently been used as a tool to investigate the
More informationInternet Appendix C: Pooled Regressions with Pre and Post Regulation-Change Samples
Internet Appendix C: Pooled Regressions with Pre and Post Regulation-Change Samples Chun Chang Shanghai Advanced Institute of Finance Shanghai Jiaotong University cchang@saif.sjtu.edu.cn Yao-Min Chiang
More informationCurrent Account Balances and Output Volatility
Current Account Balances and Output Volatility Ceyhun Elgin Bogazici University Tolga Umut Kuzubas Bogazici University Abstract: Using annual data from 185 countries over the period from 1950 to 2009,
More informationPublic Employees as Politicians: Evidence from Close Elections
Public Employees as Politicians: Evidence from Close Elections Supporting information (For Online Publication Only) Ari Hyytinen University of Jyväskylä, School of Business and Economics (JSBE) Jaakko
More informationARIMA ANALYSIS WITH INTERVENTIONS / OUTLIERS
TASK Run intervention analysis on the price of stock M: model a function of the price as ARIMA with outliers and interventions. SOLUTION The document below is an abridged version of the solution provided
More informationFor Online Publication Additional results
For Online Publication Additional results This appendix reports additional results that are briefly discussed but not reported in the published paper. We start by reporting results on the potential costs
More information1) The Effect of Recent Tax Changes on Taxable Income
1) The Effect of Recent Tax Changes on Taxable Income In the most recent issue of the Journal of Policy Analysis and Management, Bradley Heim published a paper called The Effect of Recent Tax Changes on
More informationAsymmetric Price Transmission: A Copula Approach
Asymmetric Price Transmission: A Copula Approach Feng Qiu University of Alberta Barry Goodwin North Carolina State University August, 212 Prepared for the AAEA meeting in Seattle Outline Asymmetric price
More informationLong Run Stock Returns after Corporate Events Revisited. Hendrik Bessembinder. W.P. Carey School of Business. Arizona State University.
Long Run Stock Returns after Corporate Events Revisited Hendrik Bessembinder W.P. Carey School of Business Arizona State University Feng Zhang David Eccles School of Business University of Utah May 2017
More informationDecimalization and Illiquidity Premiums: An Extended Analysis
Utah State University DigitalCommons@USU All Graduate Plan B and other Reports Graduate Studies 5-2015 Decimalization and Illiquidity Premiums: An Extended Analysis Seth E. Williams Utah State University
More informationLiquidity Variation and the Cross-Section of Stock Returns *
Liquidity Variation and the Cross-Section of Stock Returns * Fangjian Fu Singapore Management University Wenjin Kang National University of Singapore Yuping Shao National University of Singapore Abstract
More informationThe Golub Capital Altman Index
The Golub Capital Altman Index Edward I. Altman Max L. Heine Professor of Finance at the NYU Stern School of Business and a consultant for Golub Capital on this project Robert Benhenni Executive Officer
More informationIdentifying Jumps in the Stock Prices of Banks and Non-bank Financial Corporations in India A Pitch
Identifying Jumps in the Stock Prices of Banks and Non-bank Financial Corporations in India A Pitch Mohammad Abu Sayeed, PhD Student Tasmanian School of Business and Economics, University of Tasmania Keywords:
More information